动手学深度学习学习笔记tf2.0版（4.2 模型参数的访问、初始化和共享）

class Linear(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.d1 = tf.keras.layers.Dense(
            units=10,
            activation=None,
            kernel_initializer=tf.zeros_initializer(),
            bias_initializer=tf.zeros_initializer()
        )
        self.d2 = tf.keras.layers.Dense(
            units=1,
            activation=None,
            kernel_initializer=tf.ones_initializer(),
            bias_initializer=tf.ones_initializer()
        )

    def call(self, input):
        output = self.d1(input)
        output = self.d2(output)
        return output

net = Linear()
net(X)
net.get_weights()

def my_init():
    return tf.keras.initializers.Ones()

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, kernel_initializer=my_init()))

Y = model(X)
model.weights[0]

4.3 参数共享

如下，构建模型，在call方法中多次使用同一个self.dense层，即共享Dense层

# 构造一个复杂点的模型
class FancyMLP(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.flatten = tf.keras.layers.Flatten()
        self.rand_weight = tf.constant(
            tf.random.uniform((20,20)))
        self.dense = tf.keras.layers.Dense(units=20, activation=tf.nn.relu)

    def call(self, inputs):         
        x = self.flatten(inputs)   
        x = tf.nn.relu(tf.matmul(x, self.rand_weight) + 1)
        x = self.dense(x)
        x = tf.nn.relu(tf.matmul(x, self.rand_weight) + 1)
        x = self.dense(x)
        while tf.norm(x) > 1:
            x /= 2
        if tf.norm(x) < 0.8:
            x *= 10
        return tf.reduce_sum(x)